SRAW-Attack: Space-Reweighted Adversarial Warping Attack for SAR Target Recognition
This addresses the problem of adversarial robustness in SAR target recognition for defense and security applications, representing an incremental improvement in attack methods.
The paper tackles the vulnerability of deep neural network-based SAR automatic target recognition systems to adversarial examples by proposing the SRAW attack, which uses optimized spatial deformation to degrade model performance while maintaining stealthiness, achieving significant reductions in accuracy across state-of-the-art models.
Synthetic aperture radar (SAR) imagery exhibits intrinsic information sparsity due to its unique electromagnetic scattering mechanism. Despite the widespread adoption of deep neural network (DNN)-based SAR automatic target recognition (SAR-ATR) systems, they remain vulnerable to adversarial examples and tend to over-rely on background regions, leading to degraded adversarial robustness. Existing adversarial attacks for SAR-ATR often require visually perceptible distortions to achieve effective performance, thereby necessitating an attack method that balances effectiveness and stealthiness. In this paper, a novel attack method termed Space-Reweighted Adversarial Warping (SRAW) is proposed, which generates adversarial examples through optimized spatial deformation with reweighted budgets across foreground and background regions. Extensive experiments demonstrate that SRAW significantly degrades the performance of state-of-the-art SAR-ATR models and consistently outperforms existing methods in terms of imperceptibility and adversarial transferability. Code is made available at https://github.com/boremycin/SAR-ATR-TransAttack.